IGNOU| RESEARCH METHODOLOGY AND STATISTICAL ANALYSIS (MCO - 01)| SOLVED PAPER – (DEC - 2023)| (M.COM)| ENGLISH MEDIUM
MASTER OF COMMERCE (M. COM.)
Term-End Examination
December - 2023
MCO–3
RESEARCH METHODOLOGY AND STATISTICAL ANALYSIS
Time: 3 Hours
Maximum Marks: 100
Weightage: 70%
Note: Attempt any five questions. All questions carry equal marks.
हिंदी माध्यम: यहां क्लिक करें
1. (i) What are the characteristics of a good research report?
Ans:- The
main characteristics of a good research report are:-
(i)
Accuracy: The information presented in the report should be based on
accurate and reliable facts and data, without any bias from the personal
feelings of the author.
(ii)
Clarity and Completeness: The report should be straightforward,
unambiguous and comprehensive, avoiding ambiguity. It should clearly state the
objectives, methodology, findings and conclusions.
(iii)
Simplicity: The language used should be simple and easy to understand,
avoiding jargon and technical terms, especially if the report is for a general
audience.
(iv)
Brevity: The report should be concise, striking a balance between being
concise enough to maintain the reader's interest and covering the subject
matter sufficiently.
(v)
Coherence: The report should have a logical flow of ideas and a coherent
order of sentences, which contributes to a smooth continuity of ideas.
(vi)
Readability: Technical reports should also be easy to understand, with the
author able to translate technical details into reader-friendly language.
(vii)
Objectivity: The report should be written in an objective style, presenting
facts without exaggeration or personal bias.
(viii)
Proper formatting: The report should have an attractive
appearance, with appropriate use of headings, subheadings, paragraphs, visual
aids, and other formatting elements to enhance clarity and readability.
By
incorporating these key features, a research report can effectively communicate
complex information, establish credibility, and enable stakeholders to make
informed decisions.
(ii)
Discuss the precautions required at the time of interpretation of data with
examples. 8+12
Ans:- Here
is a brief response on the precautions required while interpreting data, with
examples:-
The main
precautions that should be taken while interpreting data are:-
(i) Ensure
that the data is from a reliable source: The data used for interpretation
should come from a reliable and trustworthy agency or organization. Using data
from unreliable sources may lead to erroneous conclusions.
(ii)
Confirm the suitability of data for the purpose: The
investigator should ensure that the data is appropriate and relevant to the
current research or investigation. The purpose, time period and geographical
coverage of the secondary data should match the needs of the study.
(iii)
Check the sufficiency and accuracy of data: It is important to use adequate and
accurate data to avoid biases and errors that may affect the findings. The
investigator should assess the sampling methods, definitions and degree of
accuracy used in collecting the data.
(iv)
Understand the context of data collection: The timing, conditions and methods
used to collect the original data should be clearly understood before
interpreting it. This helps to assess the relevance and reliability of the
data.
(v)
Compare data from multiple sources: Comparing secondary data to other
similar data sets can help validate findings and identify any discrepancies.
For
example, a researcher studying customer satisfaction may use a customer
satisfaction dashboard that brings together quantitative metrics such as NPS
and qualitative feedback. Correctly interpreting this data requires
understanding the data sources, collection methods and limitations to draw
accurate conclusions about customer sentiment.
Similarly, when
using secondary census data, the investigator must ensure that the definitions,
geographic coverage and accuracy of the data match the needs of their research.
By
following these precautions, researchers can ensure that the data
interpretation process leads to reliable and meaningful findings.
2. (i) What is survey research? How is it different from
observation research?
Ans:- Survey
research and observational research are two different methods of data
collection that are used in various fields, including social sciences, market
research, and health research.
Here are
the main differences:-
(i) Survey
research: Survey research involves gathering information about a group of
people by asking them questions and analyzing the results. It is a quantitative
method that uses structured survey questions to collect data from a sample of
respondents. Surveys can be conducted through various methods, such as mail,
online, or through personal interviews. The data collected is then analyzed
statistically to draw meaningful conclusions.
Key
features of survey research:-
(i) Active
method: Surveys involve direct interaction with respondents, where
researchers ask questions to collect information.
(ii)
Structured questions: Surveys use pre-designed questions to collect
specific data.
(iii)
Large sample size: Surveys can be used to collect data from
large and diverse populations.
(iv)
Subjective data: Surveys are prone to biases due to social
desirability and response biases, as respondents may not always provide
accurate information.
(ii)
Observational research: Observational research, on the other hand,
involves collecting data by directly observing the behaviour of individuals or
groups in their natural surroundings, without asking any questions. It is a
qualitative method that uses observation to collect detailed and objective
information about a specific behaviour or system.
Key
features of observational research:-
(i)
Passive method: Observation involves minimal or no direct contact with the
subjects being observed.
(ii)
Unstructured data: Observation often involves collecting
detailed and nuanced information about behaviour without pre-determined
questions.
(iii)
Small sample size: Observations are generally used to collect
data from a smaller, more specific population.
(iv)
Objective data: Observations are less prone to biases, as they rely on direct
observation rather than self-reported data.
In short, survey
research is an active method that uses structured questions to collect
subjective data from a large sample, while observational research is a passive
method that uses direct observation to collect objective data from a small
sample. Each method has its own strengths and limitations, and researchers
choose the appropriate method based on their research goals and objectives.
(ii)
Discuss the sources of sampling and non-sampling errors with suitable examples.
10+10
Ans:- The
major sources of sampling and non-sampling errors are:-
(i)
Sampling errors:
(a) Faulty
selection of sampling method – For example, if a company uses a
non-random sampling method instead of a random one, it may introduce bias in
the sample.
(b) Faulty
demarcation of sampling units – If the population is not clearly
defined, the sample may not be representative.
(c)
Variability in population characteristics – If there is high variability in the
population, the sample may not accurately reflect the true population
parameters.
(d)
Substituting one sample for another due to difficulties in data collection – This may
happen when the original sample is hard to access, leading to a less
representative option.
To reduce
sampling errors, researchers can:-
(i)
Increase the sample size, as larger samples are generally more representative.
(ii)
Increase the sample size, as larger samples are generally more representative.
(iii)
Divide the population into groups (strata) and sample from each group.
(iv) Use
random selection to eliminate bias.
(v)
Perform external checks to validate the sample.
(ii)
Non-sampling errors:-
(a)
Response errors - Incorrect responses from survey participants
due to poor questionnaire design, misinterpretation of questions or respondent
bias.
(b)
Respondent errors - Mistakes made by survey participants in
providing information.
(c)
Interviewer errors - Errors made by interviewers in data
collection, such as incorrect recording of responses.
(d)
Incomplete coverage - Failure to include all relevant units in the
population, resulting in under- or over-representation.
(e) Biased
investigators - Investigators introduce their own biases into the data collection
process.
(f) Vague
or ambiguous questionnaires - Incorrectly designed survey instruments lead
to incorrect responses.
(g) Faulty
sampling frame - Errors in the list or map used to identify
sampling units.
(h) Errors
in data processing – mistakes made during coding, tabulation or
analysis of data.
(i) Recall
errors - respondents fail to remember past events accurately.
To
minimize non-sampling errors, researchers can:-
(i) Use
random selection to eliminate bias.
(ii) Train
the data collection team thoroughly.
(iii)
Conduct external checks to validate the data.
In short, sampling
errors arise from the process of selecting the sample, while non-sampling
errors can occur at any stage of the research process, from study design to
data analysis. Careful planning and execution are required to minimize both
types of errors and ensure the validity and reliability of research findings.
3. (i) Explain briefly the additive and multiplicative models of
time series. Which of these models is more commonly used and why?
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